The disclosure relates to optimizing operation of buildings containing energy consuming or energy generating devices.
Energy usage is a critical issue going forward, with ever-growing pressure to reduce dependence on energy imports, to reduce carbon emissions and other pollutants, and of most immediate impact to most energy consumers, to reduce cost. From the standpoint of the energy supplier, e.g., a public utility, infrastructure costs are driven ever higher by increasing levels of peak demand and the need to supply that peak demand with generating capacity that sits idle for a large percentage of the time.
On the grid side of the electric meter, varying electrical demand dictates significant complexity and inefficiency in electric system investment, operations, and markets. For example:                Long development lead-times and long-lived assets means that the electric system was built for different energy policies and a different demand than it currently serves.        The daily variation of the electrical demand is substantial and inefficient—upward demand excursions require the investment in and dispatch inefficient peaking plants, while downward excursions at night impair the operation of efficient and environmentally sound baseload power plants. “Unit commitment” dictates that grid operators forecast all of this a day in advance.        Hourly and shorter variation of electrical demand is also substantial and inefficient—requiring a complex array of generating technologies and ancillary service markets to perfectly balance demand and supply in the electrical grid at any time.        Conventional demand response technology is only deployed to avert conditions of grid stress—curtailing customer demand during a relatively small number of days . . . and not focused on unlocking efficiency potentials on the supply side.        
Going forward, the introduction of large quantities of intermittent renewable energy sources will exacerbate these grid operation challenges and the associated inefficiencies.
On the building side of the electric meter, optimization of large commercial building design and operations is increasingly sophisticated, with many competing companies and technologies providing significant value. However, such optimization stops at the meter. Its objective is to minimize end-use consumption and expense at the retail meter. Implicit in this thinking is that all kWhs are the same or, at best, differentiated broadly by on or off-peak prices. They are not. Instead, kWhs are produced season to season, day to day, and hour to hour from a wide range of generating resources; with different fuels, efficiencies, environmental emissions, and costs; and subject to varying states of grid congestion, especially in the urban core.
Also implicit in this thinking is that electricity is an hourly commodity. It is not. Instead, grid supply and demand is synchronized second to second by frequency response; minute to minute by regulation; and then by balancing markets that clear every five minutes. In addition, spinning reserves and inefficient combustion turbines are deployed for rapid load swings and generator forced outages.
As a result, especially in congested metropolitan areas, the retail meter boundary masks the significant complexity and inefficiency of electric system investment, operations, and markets—and so fosters only superficial commercial building contribution to grid economy, efficiency, reliability, and environmental performance.
Prior art solutions for optimizing building operations are limited in their ability to address these shortcomings in today's conditions.
It has been proposed to couple a building control and automation system with a software model of the building used to predict or simulate building energy use as a function of multiple possible building control signals, while an optimization algorithm operating in conjunction with the building model selects an optimum set of control signals to minimize energy use or expense at the meter, in an approach known as model predictive control (MPC). See, e.g., Henze, G. P., D. Kalz, S. Liu, and C. Felsmann (2005) “Experimental Analysis of Model-Based Predictive Optimal Control for Active and Passive Building Thermal Storage Inventory.” HVAC&R Research, Vol. 11, No. 2, pp. 189-214. However, both the building model and optimization algorithm are limited, and as a result prior art MPC systems are limited in the number and size of buildings they can control, the number and type of building and environmental dynamics that can be controlled, and the number and type of objectives to be served.
In addition, utility supply has evolved to the point that there is an industry of “grid services” which can be exploited by suitable control of building operations. No building control system to date has properly exploited these grid services opportunities.
There is a need, then, for an energy consumption/generation optimization system that addresses these and other shortcomings.